Object classification with basic computer vision techniques.
Final assignment for the computer vision course I took at university. The task was to classify objects in a picture using any tools we learned during the course. I used histogram equalization, Otsu thresholding, image dilation and other morphological operators for preprocessing and OpenCV's connected component labelling function for classifying objects according to their Hu moments.
Re-implementation of the paper 'Grokking: Generalization beyond overfitting on small algorithmic datasets' Paper Original paper can be found here Data
CReST in Tensorflow 2 Code for the paper: "CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning" by Chen Wei, Ki
Federated Learning Based on Dynamic Regularization This is implementation of Federated Learning Based on Dynamic Regularization. Requirements Please i
MLP GPT - Jax (wip) A GPT, made only of MLPs, in Jax. The specific MLP to be used are gMLPs with the Spatial Gating Units. Working Pytorch implementat
Summary Loop This repository contains the code for ACL2020 paper: The Summary Loop: Learning to Write Abstractive Summaries Without Examples. Training
Bellman Website | Twitter | Documentation (latest) What does Bellman do? Bellman is a package for model-based reinforcement learning (MBRL) in Python,
PyEMD: Fast EMD for Python PyEMD is a Python wrapper for Ofir Pele and Michael Werman's implementation of the Earth Mover's Distance that allows it to
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LNSNet Overview Official implementation of Learning the Superpixel in a Non-iterative and Lifelong Manner (CVPR'21) Learning Strategy The proposed LNS
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🚗 INGI Dakar 2K21 - Be the first one on the finish line ! 🚗 This year's first semester Club Info challenge will put you at the head of a car racing
Bi-level feature alignment for versatile image translation and manipulation (Under submission of TPAMI) Preparation Clone the Synchronized-BatchNorm-P
Dark Finix - Hacking Framework. Dark Finix is a all in one hacking framework wit
A flexible neural net training library inspired by fast.ai
Active Offline Policy Selection This is supporting example code for NeurIPS 2021 paper Active Offline Policy Selection by Ksenia Konyushkova*, Yutian
A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions Kapoutsis, A.C., Chatzichristofis,
WeaSEL: Weakly Supervised End-to-end Learning This is a PyTorch-Lightning-based framework, based on our End-to-End Weak Supervision paper (NeurIPS 202
Proximal Backpropagation Proximal Backpropagation (ProxProp) is a neural network training algorithm that takes implicit instead of explicit gradient s
Safe Policy Optimization with Local Feature (SPO-LF) This is the source-code for implementing the algorithms in the paper "Safe Policy Optimization wi
2017 VQA Challenge Winner (CVPR'17 Workshop) pytorch implementation of Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challeng
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